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Channel Estimation for Beyond Diagonal RIS Exploiting Core Tensor Sparsity
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DOI：10.1109/lwc.2026.3708040.png)
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Abstract
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Beyond diagonal reconfigurable intelligent surfaces (BD-RISs) enhance wave manipulation through inter-element couplings but pose significant channel estimation challenges due to cascaded channels and block-Kronecker structures. This letter proposes a compressive sensing framework exploiting the sparse Tucker decomposition of the measurement tensor and the Kronecker rank-one structure of channel components. Two algorithms are developed: Sparse Tensor Orthogonal Recovery Method (STORM), which uses orthogonal matching pursuit (OMP) for greedy support recovery, and Sparse Tensor subspace-Aided Recovery (STAR), which leverages subspace-based projection to enhance robustness to noise. Both perform joint sparse support identification, followed by a Kronecker rank-one factorization via singular value decomposition (SVD) to recover the channel parameters. Simulations show that STAR achieves oracle-assisted least squares (LS) performance at moderate-tohigh signal-to-noise ratio (SNR) with significantly fewer measurements than baseline methods, enabling practical BD-RIS deployment in next-generation millimeter wave (mmWave)/subterahertz (sub-THz) networks.
Keywords:
BD-RIS
compressive sensing (CS)
mmWave
reconfigurable intelligent surface (RIS)
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